🎧 CTT: Compressed Tensor/Transform

CTT is an ultra-lightweight, zero-retraining post-training model compression framework. It achieves extreme model reduction (~30x compression ratios) while preserving structural layer integrity and offering instantaneous compression and decompression.

Unlike traditional post-training quantization (PTQ) techniques that require heavy calibration datasets or cause catastrophic accuracy drops, CTT treats weights as an information-theoretic signal payload—combining percentile outlier shielding, dense sub-byte bit-packing, and entropy coding.

📊 Benchmark Results

Evaluated out-of-the-box on standard torchvision pre-trained models:

🚀 How It Works

1. Percentile Outlier Shielding: Automatically clips extreme weight spikes using the 99.9th absolute percentile, stabilizing the distribution map.
2. 2-Bit Non-Uniform Quantization: Maps continuous weights precisely into a 4-level discrete space over $[-1, 1]$.
3. Dense Bit-Packing: Shifting math packs four distinct 2-bit values into a single physical byte, bypassing standard hardware memory bloat.
4. Entropy Encoding: Passes the packed stream through level-9 zlib compression to crush redundant patterns.

📦 Quick Start

import torch
import torchvision.models as models
from ctt_compressor import CTTCompressor

# Load a pretrained model
model = models.resnet18(weights=models.ResNet18_Weights.DEFAULT)
compressor = CTTCompressor(target_bits=2)

# Compress a layer tensor
tensor_data = model.conv1.weight.detach().cpu().numpy()
compressed_payload = compressor.compress(tensor_data)

# Decompress back to float32 tensor
reconstructed_data = compressor.decompress(compressed_payload)